Comparer des méthodes

	Global Spatial Error Model	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Analyse spatiale	Économétrie
Famille	Regression model	Regression model
Année d'origine	1988	2019
Auteur d'origine	Luc Anselin	Wooldridge (textbook treatment); classical least squares
Type	Spatial regression model	Linear regression
Source fondatrice	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	SEM, spatial error model, spatial error regression, global SEM	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5
Résumé	The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).